Artificial intelligence possesses the capacity to achieve incredible results, but cannot always work alone. Researchers identified two key components in successful human-machine collaboration that may enhance how the U.S. Army will fight in the future. To achieve dominance in what is known as multi-domain operations, warfighters will need a layered intelligence, surveillance and reconnaissance, or ISR, network that maintains a functional relationship between autonomous sensors, human intelligence and friendly special operations forces. Multi-domain operations, known as MDO, is a joint warfighting concept that foresees conflict occurring in multiple domains: land, air, sea, cyber and space. The concept has many nuances, but basically describes how the Army, as part of the joint force, will solve the problem of layered standoff in all domains.

Reliable uncertainty estimates are an important tool for helping autonomous agents or human decision makers understand and leverage predictive models. However, existing approaches to estimating uncertainty largely ignore the possibility of covariate shift--i.e., where the real-world data distribution may differ from the training distribution. As a consequence, existing algorithms can overestimate certainty, possibly yielding a false sense of confidence in the predictive model. We propose an algorithm for calibrating predictions that accounts for the possibility of covariate shift, given labeled examples from the training distribution and unlabeled examples from the real-world distribution. Our algorithm uses importance weighting to correct for the shift from the training to the real-world distribution. However, importance weighting relies on the training and real-world distributions to be sufficiently close. Building on ideas from domain adaptation, we additionally learn a feature map that tries to equalize these two distributions. In an empirical evaluation, we show that our proposed approach outperforms existing approaches to calibrated prediction when there is covariate shift.

There is little argument about whether or not machine learning models are useful for applying to social systems. But if we take seriously George Box's dictum, or indeed the even older one that "the map is not the territory' (Korzybski, 1933), then there has been comparatively less systematic attention paid within the field to how machine learning models are wrong (Selbst et al., 2019) and seeing possible harms in that light. By "wrong" I do not mean in terms of making misclassifications, or even fitting over the'wrong' class of functions, but more fundamental mathematical/statistical assumptions, philosophical (in the sense used by Abbott, 1988) commitments about how we represent the world, and sociological processes of how models interact with target phenomena. This paper takes a particular model of machine learning research or application: one that its creators and deployers think provides a reliable way of interacting with the social world (whether that is through understanding, or in making predictions) without any intent to cause harm (McQuillan, 2018) and, in fact, a desire to not cause harm and instead improve the world, 1 for example as most explicitly in the various "{Data [Science], Machine Learning, Artificial Intelligence} for [Social] Good" initiatives, and more widely in framings around "fairness" or "ethics." I focus on the almost entirely statistical modern version of machine learning, rather than eclipsed older visions (see section 3). While many of the limitations I discuss apply to the use of machine learning in any domain, I focus on applications to the social world in order to explore the domain where limitations are strongest and stickiest.

The ability to explain decisions made by AI systems is highly sought after, especially in domains where human lives are at stake such as medicine or autonomous vehicles. While it is always possible to approximate the input-output relations of deep neural networks with human-understandable rules or a post-hoc model, the discovery of the double descent phenomena suggests that no such approximation will ever map onto the actual mechanistic functioning of deep neural networks. Double descent indicates that deep neural networks typically operate by smoothly interpolating between data points rather than by extracting a few high level rules. As a result neural networks trained on complex real world data are inherently hard to interpret and prone to failure if used outside their domain of applicability (ie, for extrapolation). To show how we might be able to trust AI despite these problems, we introduce the concept of self-explaining AI. Self-explaining AIs are capable of providing a human-understandable explanation of each decision along with confidence levels for both the decision and explanation. Some difficulties to this approach along with possible solutions are sketched. Finally, we argue it is also important that AI systems warn their user when they are asked to perform outside their domain of applicability.

Technology giant Cisco has unveiled a new Internet of Things (IoT) security architecture with the goal of protecting processes and increasing visibility in numerous industries. Aside from the obvious security benefits, increased visibility can also help executive make better, informed choices. The innovations within the IoT security architecture include Cisco Cyber Vision, which secures the industrial network environment, and Cisco Edge Intelligence, essentially data governance from edge to the multi-cloud. Cisco Cyber Vision is a solution for automated discovery of industrial assets. It analyses traffic from connected devices, creates segmentation policies to prevent hackers from moving laterally throughout the network and provides real-time monitoring of cybersecurity threats to assets. Cisco Edge Intelligence makes extracting data at the network edge simpler, as it streamlines data delivery to multi-cloud and on-premises destinations.

To support Thai policymakers in navigating this transition, the Institute of Public Policy and Development commissioned The Economist Intelligence Unit to conduct a foresight exercise that investigates how AI could affect key social and economic metrics in Thailand across three scenarios. In each of these scenarios, we have assumed that AI technology will substantially increase the use of computers and raise productivity. We focused our analysis on two critical and uncertain factors: the effectiveness of industrial policy and the extent of skills development in an AI-augmented economy. To better measure the magnitude of these impacts, we built an econometric model that forecasts four social and economic metrics to 2035: GDP growth, employment, productivity, and the relative importance of different sectors in the economy (industry, services and agriculture). We used qualitative analysis to build out our scenarios, considering the implications of AI and AI-assisted automation for specific sub-sectors of the economy, as well as wages, inequality, and social and political impacts.

"Today, we are presenting our ambition to shape Europe's digital future. It covers everything from cybersecurity to critical infrastructures, digital education to skills, and democracy to media. I want that digital Europe reflects the best of Europe – open, fair, diverse, democratic, and confident," says Ursula von der Leyen, President of the Commission. Ursula von der Leyen says that Europe needs to step up its efforts to create a truly digital economy and to make better use of the massive amount of data being collected. She argued that in five years, Europe alone would generate the same amount of data that is today collected worldwide.

Defense One's Patrick Tucker joins the podcast to discuss the set of rules to govern how the Defense Department develops and uses AI. DoD adopts new ethical principles for the use of artificial intelligence – SpaceNews.com

Luddy School of Informatics, Computing, and Engineering dean Raj Acharya poses for a headshot. Acharya will step down mid-March to participate in an artificial intelligence research initiative at IU. Courtesy of Indiana University Dean of the Luddy School of Informatics, Computing, and Engineering Raj Acharya will step down mid-March to participate in an artificial intelligence research initiative. Acharya said the school will hire an acting dean to replace him and then conduct a national search to find a permanent dean. Acharya launched the Department of Intelligent Systems Engineering in 2016 and has been dean since July 2016. He will now be associate vice president for research with the specific task of promoting artificial intelligence.

Currently, high-dimensional data is ubiquitous in data science, which necessitates the development of techniques to decompose and interpret such multidimensional (aka tensor) datasets. Finding a low dimensional representation of the data, that is, its inherent structure, is one of the approaches that can serve to understand the dynamics of low dimensional latent features hidden in the data. Nonnegative RESCAL is one such technique, particularly well suited to analyze self-relational data, such as dynamic networks found in international trade flows. Nonnegative RESCAL computes a low dimensional tensor representation by finding the latent space containing multiple modalities. Estimating the dimensionality of this latent space is crucial for extracting meaningful latent features. Here, to determine the dimensionality of the latent space with nonnegative RESCAL, we propose a latent dimension determination method which is based on clustering of the solutions of multiple realizations of nonnegative RESCAL decompositions. We demonstrate the performance of our model selection method on synthetic data and then we apply our method to decompose a network of international trade flows data from International Monetary Fund and validate the resulting features against empirical facts from economic literature.